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Real Time RT-PCR

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

PCR

PCR

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Amplification Curve Analysis: Data-Driven Multiplexing Using Real-Time Digital PCR.

Ahmad Moniri¹, Luca Miglietta¹, Kenny Malpartida-Cardenas¹

¹Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K.

Analytical Chemistry

|September 18, 2020

Summary

This summary is machine-generated.

We developed amplification curve analysis (ACA), a machine learning method for digital PCR (dPCR), to extract more data from amplification curves. This approach enables cost-effective multiplexing in a single channel, improving accuracy for detecting antimicrobial resistance genes.

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Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres

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Published on: October 23, 2011

Area of Science:

Molecular Biology
Biotechnology
Bioinformatics

Background:

Digital PCR (dPCR) typically simplifies amplification data into binary outputs, neglecting kinetic information within amplification curves.
Existing dPCR methods often require multiple fluorescent channels for multiplexing, increasing assay cost and complexity.

Purpose of the Study:

To develop a machine learning-based approach, amplification curve analysis (ACA), to extract kinetic information from dPCR amplification curves.
To enable data-driven multiplexing in a single fluorescent channel using ACA, reducing assay costs and complexity.
To demonstrate the utility of ACA for detecting carbapenem-resistant genes and addressing antimicrobial resistance.

Main Methods:

Utilized raw real-time dPCR data, specifically amplification curve kinetics, for analysis.
Applied machine learning algorithms to classify amplification events based on curve characteristics.
Employed an intercalating dye (EvaGreen) for multiplexing and melting curve analysis for validation.

Main Results:

Achieved 99.1% classification accuracy for single targets and 92.9% accuracy for all amplification event combinations (including coamplifications).
Demonstrated a 19.7% accuracy improvement compared to traditional multiplexing based on final fluorescence intensity.
Developed a formula using multivariate Poisson statistics to estimate coamplification occurrence in dPCR.

Conclusions:

Amplification curve analysis (ACA) effectively extracts valuable kinetic information from dPCR data for accurate, cost-effective multiplexing.
ACA enhances the capabilities of existing real-time dPCR instruments and chemistries, particularly for applications like antimicrobial resistance surveillance.
ACA holds potential for expanding dPCR applications beyond the laboratory, especially with emerging point-of-care technologies.